OceanBaseデータベースとの安全なやり取りを可能にするモデルコンテキストプロトコル（MCP）サーバー。このサーバーにより、AIアシスタントは制御されたインターフェースを介してテーブルの一覧表示、データの読み取り、SQLクエリの実行が可能になり、データベースの探索と分析をより安全かつ構造化できます。
利用可能な OceanBase テーブルをリソースとして一覧表示する
表の内容を読む
適切なエラー処理でSQLクエリを実行する
環境変数による安全なデータベースアクセス
包括的なログ記録
Related MCP server: Microsoft SQL Server MCP Server
pip install oceanbase-mcp-server次の環境変数を設定します。
OB_HOST=localhost # Database host
OB_PORT=2881 # Optional: Database port (defaults to 2881 if not specified)
OB_USER=your_username
OB_PASSWORD=your_password
OB_DATABASE=your_databaseこれをclaude_desktop_config.jsonに追加します:
{
"mcpServers": {
"oceanbase": {
"command": "uv",
"args": [
"--directory",
"path/to/oceanbase_mcp_server",
"run",
"oceanbase_mcp_server"
],
"env": {
"OB_HOST": "localhost",
"OB_PORT": "2881",
"OB_USER": "your_username",
"OB_PASSWORD": "your_password",
"OB_DATABASE": "your_database"
}
}
}
}# Install dependencies
pip install -r requirements.txt
# Run the server
python -m oceanbase_mcp_server# Clone the repository
git clone https://github.com/yourusername/oceanbase_mcp_server.git
cd oceanbase_mcp_server
# Create virtual environment
python -m venv venv
source venv/bin/activate # or `venv\Scripts\activate` on Windows
# Install development dependencies
pip install -r requirements-dev.txt
# Run tests
pytest環境変数や資格情報をコミットしないでください
必要最小限の権限を持つデータベースユーザーを使用する
本番環境での使用にクエリホワイトリストを実装することを検討する
すべてのデータベース操作を監視して記録する
このMCPサーバーは機能するためにデータベースへのアクセスが必要です。セキュリティのため：
最小限の権限を持つ専用のOceanBaseユーザーを作成する
ルート認証情報や管理者アカウントは使用しないでください
必要な操作のみにデータベースアクセスを制限する
監査目的でログを有効にする
データベースアクセスの定期的なセキュリティレビュー
詳細な手順については、 OceanBase セキュリティ構成ガイドを参照してください。
制限付きOceanBaseユーザーの作成
適切な権限の設定
データベースアクセスの監視
セキュリティのベストプラクティス
⚠️ 重要: データベース アクセスを構成するときは、常に最小権限の原則に従ってください。
Apache ライセンス - 詳細については LICENSE ファイルを参照してください。
リポジトリをフォークする
機能ブランチを作成します（ git checkout -b feature/amazing-feature ）
変更をコミットします ( git commit -m 'Add some amazing feature' )
ブランチにプッシュする ( git push origin feature/amazing-feature )
プルリクエストを開く
Execute an SQL query on the OceanBase server
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The SQL query to execute |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions execution but fails to address critical aspects like required permissions, transaction handling, error behavior, or result format. This leaves significant gaps for a tool that performs database operations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary elaboration. It's appropriately sized and front-loaded, with zero wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a database execution tool with no annotations and no output schema, the description is insufficient. It lacks information about return values, error handling, security requirements, and operational constraints, which are critical for proper tool invocation in this context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'query' documented in the schema. The description adds no additional parameter semantics beyond what the schema already provides, so it meets the baseline for adequate but unenhanced documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('execute') and resource ('SQL query on the OceanBase server'), providing a specific verb+resource combination. However, with no sibling tools mentioned, there's no opportunity to distinguish from alternatives, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or limitations. It merely states what the tool does without context for application, leaving the agent to infer usage scenarios independently.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0execute_sqlScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'execute_sql' has a clear and distinct purpose that cannot be confused with any other tool in this set.
The single tool name 'execute_sql' follows a clear verb_noun pattern, and with only one tool, consistency is inherently perfect. There are no other tools to compare against, so no inconsistencies can exist.
A single tool is too few for a database server's apparent scope, which typically requires operations like querying, updating, schema management, and transaction handling. This minimal set severely limits functionality and will likely cause agent failures in complex tasks.
The tool surface is severely incomplete for a database server domain. It only provides SQL execution, missing essential operations such as listing tables, describing schemas, creating/dropping tables, managing transactions, or handling connections, leaving significant gaps that will hinder agent workflows.
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